Enhancing Semantic Understanding with Self-Supervised Methods for Abstractive Dialogue Summarization

FOS: Computer and information sciences Computer Science - Computation and Language Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence 0202 electrical engineering, electronic engineering, information engineering 02 engineering and technology Computation and Language (cs.CL)
DOI: 10.21437/interspeech.2021-1270 Publication Date: 2021-08-27T01:59:39Z
ABSTRACT
5 pages, 3 figures, INTERSPEECH 2021<br/>Contextualized word embeddings can lead to state-of-the-art performances in natural language understanding. Recently, a pre-trained deep contextualized text encoder such as BERT has shown its potential in improving natural language tasks including abstractive summarization. Existing approaches in dialogue summarization focus on incorporating a large language model into summarization task trained on large-scale corpora consisting of news articles rather than dialogues of multiple speakers. In this paper, we introduce self-supervised methods to compensate shortcomings to train a dialogue summarization model. Our principle is to detect incoherent information flows using pretext dialogue text to enhance BERT's ability to contextualize the dialogue text representations. We build and fine-tune an abstractive dialogue summarization model on a shared encoder-decoder architecture using the enhanced BERT. We empirically evaluate our abstractive dialogue summarizer with the SAMSum corpus, a recently introduced dataset with abstractive dialogue summaries. All of our methods have contributed improvements to abstractive summary measured in ROUGE scores. Through an extensive ablation study, we also present a sensitivity analysis to critical model hyperparameters, probabilities of switching utterances and masking interlocutors.<br/>
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